Behavioral targeting is a technique that is used to increase the effectiveness of advertisements, or goods and services offerings, to be presented to a potential consumer (a.k.a. a user) based on historic behaviors (e.g. actions, activities) by the user. Analysis of the user's previous actions in on-line interactions (e.g. web pages viewed, multimedia items selected, and products purchased) using rules or algorithms can result in a characterization of the user's preferences that can be embodied in a user profile.
Typically the rules or algorithms used to generate a user profile (i.e. profiling) are adapted to the specific domain (e.g. searching or viewing Internet web pages) in which the user is participating. The rules or algorithms are typically derived from the analysis of the behaviors of a set of users having activities in the specific domain. In the previous art, the rules or algorithms used in the analysis have been the result of either human development or machine-learning Human developed rules or algorithms are labor intensive and can be error prone. Machine-learned rules derived by data mining of user historic data can be computing intensive, time consuming and as such is done as an off-line (i.e. not real-time) activity.
Accordingly, method and system that enable improved machine-learning based profiling of user on-line actions remains highly desirable.